Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

Abstract

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl3-calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. The models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

“…Deep-learning algorithms have recently gained increasing popularity for assisting XRD interpretation, as they can effectively extract the latent information that is often hard to manually capture from the diffraction patterns. − Early pioneering studies have primarily focused on the autonomous inference of structural attributes from the XRD patterns, including lattice parameters, space group, − and crystallographic dimensionality . These attributes can be fed to the traditional rule-based approaches to speed up the process of XRD analysis.…”